The Emergence of Integrated Information, Complexity, and ‘Consciousness’ at Criticality

Abstract

Integrated Information Theory (IIT) posits that integrated information ( Φ ) represents the quantity of a conscious experience. Here, the generalized Ising model was used to calculate Φ as a function of temperature in toy models of fully connected neural networks. A Monte–Carlo simulation was run on 159 normalized, random, positively weighted networks analogous to small five-node excitatory neural network motifs. Integrated information generated by this sample of small Ising models was measured across model parameter spaces. It was observed that integrated information, as an order parameter, underwent a phase transition at the critical point in the model. This critical point was demarcated by the peak of the generalized susceptibility (or variance in configuration due to temperature) of integrated information. At this critical point, integrated information was maximally receptive and responsive to perturbations of its own states. The results of this study provide evidence that Φ can capture integrated information in an empirical dataset, and display critical behavior acting as an order parameter from the generalized Ising model.

Avalanche criticality in individuals, fluid intelligence and working memory

“…This left us with 295 subjects for further analysis, and 162 of them were females. All the participants were between the ages of 22-36, 58 were between the ages of [22][23][24][25]130 were between the ages of [26][27][28][29][30]104 were between the ages of 31-35, and 3 were 36 years old.…”

“…For example, it has been shown that structural connections of brains are mostly reflected in functional networks, and this structure-function coupling is disrupted when brains move away from criticality during anesthesia (Tagliazucchi et al, 2016). Another application of criticality is to explain the dynamic basis of brain complexity (Popiel, 2020;Tagliazucchi and Chialvo, 2013;Timme et al, 2016). In particular, functional connectivity (FC) complexity, which is an umbrella term describing the variability, diversity, or flexibility of functional connections in brain networks, has been associated with cognitive performance from many perspectives, such as high-order cognition, aging, and cognitive impairment in brain disorders (Ahmadlou et al, 2014;Anokhin et al, 1996;Omidvarnia et al, 2019;Smyser et al, 2016;Wang et al, 2017).…”

Computing Integrated Information (Φ) in Discrete Dynamical Systems with Multi-Valued Elements

“…The more integrated information that a system brings about, the more it can be regarded as a unitary whole as opposed to merely a collection of parts, which makes a measure of causal autonomy [ 21 , 22 ]. Because a system with high needs to be both strongly integrated and differentiated (informative) [ 23 ], may also serve as a general measure of complexity [ 24 , 25 , 26 ]. Consequently, IIT’s mathematical framework has proven to be useful and relevant for research on complexity [ 24 , 27 , 28 ], emergence [ 29 , 30 ], and certain biological questions [ 22 ], in addition to the study of consciousness.…”

Towards a Universal Measure of Complexity

“…Analysis of the concept of complexity is a non-trivial task due to its diversity, arbitrariness, uncertainty, and contextual nature [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. There are many different levels/scales, faces, and types of complexity, researched with very different technologies/techniques and tools [ 11 , 12 , 13 ] (and refs. therein).…”